Real-Time Analytical Processing (RTAP) Using the Spark Stack Jason Dai

Real-Time Analytical Processing (RTAP)
Using the Spark Stack
Jason Dai
[email protected]
Intel Software and Services Group
Project Overview
Research & open source projects initiated by AMPLab
in UC Berkeley
BDAS: Berkeley Data Analytics Stack
(Ref: https://amplab.cs.berkeley.edu/software/)
Intel closely collaborating with AMPLab & the community
on open source development
• Apache incubation since June 2013
• The “most active cluster data processing engine
after Hadoop MapReduce”
Intel partnering with several big websites
• Building next-gen big data analytics using the Spark stack
• Real-time analytical processing (RTAP)
• E.g., Alibaba , Baidu iQiyi, Youku, etc.
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Next-Gen Big Data Analytics
Volume
• Massive scale & exponential growth
Variety
• Multi-structured, diverse sources & inconsistent schemas
Value
• Simple (SQL) – descriptive analytics
• Complex (non-SQL) – predictive analytics
Velocity
• Interactive – the speed of thought
• Streaming/online – drinking from the firehose
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Next-Gen Big Data Analytics
Volume
• Massive scale & exponential growth
Variety
• Multi-structured, diverse sources & inconsistent schemas
Value
• Simple (SQL) – descriptive analytics
• Complex (non-SQL) – predictive analytics
Velocity
• Interactive – the speed of thought
• Streaming/online – drinking from the firehose
4
The Spark stack
Real-Time Analytical Processing (RTAP)
A vision for next-gen big data analytics
• Data captured & processed in a (semi) streaming/online fashion
• Real-time & history data combined and mined interactively and/or iteratively
– Complex OLAP / BI in interactive fashion
– Iterative, complex machine learning & graph analysis
• Predominantly memory-based computation
Events /
Logs
Messaging /
Queue
Stream
Processing
In-Memory
Store
Persistent Storage
NoSQL
Data
Warehouse
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Online Analysis
/ Dashboard
Low Latency
Processing
Engine
Ad-hoc,
interactive
OLAP / BI
Iterative, complex
machine learning &
graph analysis
Real-World Use Case #1
(Semi) Real-Time Log Aggregation & Analysis
Logs continuously collected & streamed in
• Through queuing/messaging systems
Incoming logs processed in a (semi) streaming fashion
• Aggregations for different time periods, demographics, etc.
• Join logs and history tables when necessary
Aggregation results then consumed in a (semi) streaming fashion
• Monitoring, alerting, etc.
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(Semi) Real-Time Log Aggregation: MiniBatch Jobs
Log Collector
Log Collector
Kafka
Client
Kafka
Client
…
Server
Kafka
Server
Server
Spark
Mesos
Spark
Kafka
Kafka
Mesos
…
Spark
Mesos
Architecture
• Log collected and published to Kafka continuously
RDBMS
– Kafka cluster collocated with Spark Cluster
• Independent Spark applications launched at regular interval
– A series of mini-batch apps running in “fine-grained” mode on Mesos
– Process newly received data in Kafka (e.g., aggregation by keys) & write results out
In production in the user’s environment today
• 10s of seconds latency
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For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
(Semi) Real-Time Log Aggregation: MiniBatch Jobs
Design decisions
• Kafka and Spark servers collocated
– A distinct Spark task for each Kafka partition, usually reading from local file system cache
• Individual Spark applications launched periodically
– Apps logging current (Kafka partition) offsets in ZooKeeper
– Data of failed batch simply discarded (other strategies possible)
• Spark running in “fine-grained” mode on Mesos
– Allow the next batch to start even if the current one is late
Performance
• Input (log collection) bottlenecked by network bandwidth (1GbE)
• Processing (log aggregation) bottlenecked by CPUs
– Easily scaled to several seconds
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For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
(Semi) Real-Time Log Aggregation: Spark
Streaming
Log
Collectors
Kafka
Cluster
Spark
Cluster
RDBMS
Implications
• Better streaming framework support
– Complex (e.g., stateful) analysis, fault-tolerance, etc.
• Kafka & Spark not collocated
– DStream retrieves logs in background (over network) and caches blocks in memory
• Memory tuning to reduce GC is critical
– spark.cleaner.ttl (throughput * spark.cleaner.ttl < spark mem free size)
– Storage level (MEMORY_ONLY_SER2)
• Lowe latency (several seconds)
– No startup overhead (reusing SparkContext)
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For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
Real-World Use Case #2
Complex, Interactive OLAP / BI
Significant speedup of ad-hoc, complex OLAP / BI
• Spark/Shark cluster runs alongside Hadoop/Hive clusters
• Directly query on Hadoop/Hive data (interactively)
• No ETL, no storage overhead, etc.
In-memory, real-time queries
• Data of interest loaded into memory
create table XYZ tblproperties ("shark.cache" = "true") as select ...
• Typically frontended by lightweight UIs
– Data visualization and exploration (e.g., drill down / up)
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Time Series Analysis: Unique Event Occurrence
Computing unique event occurrence across the time range
• Input time series
<TimeStamp, ObjectId, EventId, ...>
– E.g., watch of a particular video by a specific user
– E.g., transactions of a particular stock by a specific account
• Output:
<ObjectId, TimeRange, Unique Event#, Unique Event(≥2)#, …, Unique Event(≥n)#>
– E.g., accumulated unique event# for each day in a week (staring from Monday)
…
1
2
…
7
• Implementation
– 2-level aggregation using Spark
• Specialized partitioning, general execution graph, in-memory cached data
– Speedup from 20+ hours to several minutes
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For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
Time Series Analysis: Unique Event Occurrence
Input time series
TimeStamp
ObjectId
EventId
…
Partitioned by (ObjectId, EventId)
Aggregation
(shuffle)
ObjectId
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EventId
Day
(1, 2, … ,7)
1
ObjectId
EventId
Day
Count
(1, 2, … ,7)
Potentially cached in mem
Time Series Analysis: Unique Event Occurrence
Input time series
TimeStamp
ObjectId
EventId
…
Partitioned by (ObjectId, EventId)
Aggregation
(shuffle)
ObjectId
EventId
Day
(1, 2, … ,7)
1
ObjectId
ObjectId
ObjectId
EventId
TimeRage
[1, D]
EventId
TimeRage
[1, D]
1
Potentially cached in mem
Day
Count
(1, 2, … ,7)
Accumulated
Count
1 (if count ≥2)
0 otherwise
…
1 (if count ≥n)
0 otherwise
Aggregation
(shuffle)
ObjectId
Partitioned by ObjectId
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TimeRage
[1, D]
Unique Event# Unique Event(≥2)#
…
Unique Event(≥n)#
Real-World Use Case #3
Complex Machine Learning & Graph Analysis
Algorithm: complex math operations
• Mostly matrix based
– Multiplication, factorization, etc.
• Sometime graph-based
– E.g., sparse matrix
Iterative computations
• Matrix (graph) cached in memory across iterations
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Graph Analysis: N-Degree Association
N-degree association in the graph
• Computing associations between two
vertices that are n-hop away
• E.g., friends of friend
Graph-parallel implementation
Weight1(u, v) = edge(u, v) ∈ (0, 1)
Weightn(u, v) = 𝑥→𝑣 Weightn−1(u, x)∗Weight1(x, v)
• Bagel (Pregel on Spark) and GraphX
– Memory optimizations for efficient graph
caching critical
• Speedup from 20+ minutes to <2 minutes
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For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
Graph Analysis: N-Degree Association
u
v
State[u] = list of Weight(x, u)
(for current top K weights to vertex u)
w
State[w] = list of Weight(x, w)
(for current top K weights to vertex w)
State[v] = list of Weight(x, v)
(for current top K weights to vertex v)
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Graph Analysis: N-Degree Association
u
v
State[u] = list of Weight(x, u)
(for current top K weights to vertex u)
w
State[w] = list of Weight(x, w)
(for current top K weights to vertex w)
State[v] = list of Weight(x, v)
(for current top K weights to vertex v)
u
Messages = {D(x, u) =
Weight(x, v) * edge(w, u)}
(for weight(x, v) in State[v])
Messages = {D(x, u) =
Weight(x, w) * edge(w, u)}
(for weight(x, w) in State[w])
v
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w
Graph Analysis: N-Degree Association
u
v
State[u] = list of Weight(x, u)
(for current top K weights to vertex u)
w
State[w] = list of Weight(x, w)
(for current top K weights to vertex w)
State[v] = list of Weight(x, v)
(for current top K weights to vertex v)
Weight(u, x) =
u
u
Messages = {D(x, u) =
Weight(x, v) * edge(w, u)}
(for weight(x, v) in State[v])
Messages = {D(x, u) =
Weight(x, w) * edge(w, u)}
(for weight(x, w) in State[w])
v
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v
w
𝐷 𝑥,𝑢 𝑖𝑛 𝑀𝑒𝑠𝑠𝑎𝑔𝑒𝑠 D(x,
u)
State[u] = list of Weight(x, u)
(for current top K weights to vertex w)
w
Graph Analysis: N-Degree Association
u
v
State[u] = list of Weight(x, u)
(for current top K weights to vertex u)
w
State[w] = list of Weight(x, w)
(for current top K weights to vertex w)
State[v] = list of Weight(x, v)
(for current top K weights to vertex v)
Weight(u, x) =
u
u
Messages = {D(x, u) =
Weight(x, v) * edge(w, u)}
(for weight(x, v) in State[v])
Messages = {D(x, u) =
Weight(x, w) * edge(w, u)}
(for weight(x, w) in State[w])
v
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v
w
𝐷 𝑥,𝑢 𝑖𝑛 𝑀𝑒𝑠𝑠𝑎𝑔𝑒𝑠 D(x,
u)
State[u] = list of Weight(x, u)
(for current top K weights to vertex w)
w
Contributions by Intel
Netty based shuffle for Spark
FairScheduler for Spark
Spark job log files
Metrics system for Spark
Configurations system for Spark
Spark shell on YARN
Spark (standalone mode) integration with security Hadoop
Byte code generation for Shark
Co-partitioned join in Shark
...
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Summary
The Spark stack: lightning-fast analytics over Hadoop data
• Active communities and early adopters evolving
– Apache incubator project
• A growing stack: SQL, streaming, graph-parallel, machine learning, …
Work with us on next-gen big data analytics using the Spark stack
• Interactive and in-memory OLAP / BI
• Complex machine learning & graph analysis
• Near real-time, streaming processing
• And many more!
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